Bayesian classification of hyperspectral images using spatially-varying Gaussian mixture model

We propose a spatially-varying Gaussian mixture model for joint spectral and spatial classification of hyperspectral images. The model provides a robust estimation framework for small sample size training sets. Defining prior distributions for the mean vector and the covariance matrix enables us to regularize the parameter estimation problem. More specifically, we can obtain invertible positive definite covariance matrices by the help of this regularization. Moreover, the proposed model also takes into account the spatial alignments of the pixels by using spatially-varying mixture proportions. The spatially-varying mixture model is based on spatial multinomial logistic regression. The classification results obtained on Indian Pines, Pavia Centre, Pavia University, and Salinas data sets show that the proposed methods perform better especially for small-sized training sets compared to the state-of-the-art classifiers. (C) 2016 Elsevier Inc. All rights reserved

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis